Information-processing device, information-processing method, and program

ABSTRACT

An information-processing device of the present invention includes an acquisition unit that acquires sensor detection information indicating a detection result from a sensor mounted in a vehicle, a derivation unit that derives a plurality of indices for a surrounding environment based on the sensor detection information acquired by the acquisition unit, and an evaluation unit that evaluates attribute information of a point at which the sensor detection information is acquired based on the plurality of indices derived by the derivation unit.

TECHNICAL FIELD

The present invention relates to an information-processing device, aninformation-processing method, and a program.

Priority is claimed on Japanese Patent Application No. 2017-118694,filed on Jun. 16, 2017, the contents of which are incorporated herein byreference.

BACKGROUND

Among navigation devices that provide an occupant with informationrelating to a route of a vehicle, a navigation device is known thatprovides not only information relating to a route to a destination, butalso information on stores near a point at which a vehicle is travelingor sightseeing information (for example, Patent Document 1).

RELATED ART DOCUMENTS Patent Documents [Patent Document 1]

Japanese Unexamined Patent Application, First Publication No.2007-285870

SUMMARY OF INVENTION Problems to be Solved by the Invention

However, in the technique of the related art, information relating to alandscape provided to an occupant is not acquired from a vehicletraveling in reality, and it is not evaluated how good a view of alandscape in the vicinity where a vehicle is traveling is in reality.

The present invention was contrived in view of such circumstances, andone object thereof is to provide an information-processing device, aninformation-processing method, and a program which make it possible toautomatically index how good a view of a landscape is on the basis ofinformation acquired from a traveling vehicle.

Means for Solving the Problem

An information-processing device according to this invention has thefollowing configurations adopted therein.

(1) An information-processing device according to an aspect of thisinvention is an information-processing device including: an acquisitionunit that acquires sensor detection information indicating a detectionresult from a sensor mounted in a vehicle (for example, an informationacquisition unit 210 of an embodiment); a derivation unit that derives aplurality of indices for a surrounding environment based on the sensordetection information acquired by the acquisition unit (for example, anindex derivation unit 220 of the embodiment); and an evaluation unitthat evaluates attribute information of a point at which the sensordetection information is acquired based on the plurality of indicesderived by the derivation unit (for example, a landscape evaluation unit230 of the embodiment).

(2) In the aspect of the above (1), the evaluation unit derives anevaluation value of a sensitivity index at the point in association witha position at which the sensor detection information is acquired basedon the plurality of indices and vehicle information including positioninformation.

(3) In the aspect of the above (2), the evaluation unit derives theevaluation value of the sensitivity index in association with a timeincluded in a unit of a predetermined time length.

(4) In the aspect of the above (2) or (3), an information-providing unitthat provides the vehicle with information relating to a landscape basedon the evaluation value is further included.

(5) In the aspect of the above (4), the information-providing unitprovides the vehicle with the information relating to the landscape inaccordance with relevance between a category of good views selected byan occupant of the vehicle and the attribute information.

(6) In any one aspect of the above (1) to (5), the vehicle is anautonomously driven vehicle, and the vehicle performs at least one of alane change or a change of a distance relationship with another vehicleso as to improve evaluation performed by the evaluation unit of how gooda view is.

(7) An information-processing method according to an aspect of thisinvention is an information-processing method causing a computer to:acquire sensor detection information indicating a detection result froma sensor mounted in a vehicle; derive a plurality of indices for asurrounding environment based on the acquired sensor detectioninformation; and evaluate attribute information of a point at which thesensor detection information is acquired based on the plurality ofindices which are derived.

(8) A program according to an aspect of this invention is a programcausing a computer to: acquire sensor detection information indicating adetection result from a sensor mounted in a vehicle; derive a pluralityof indices for a surrounding environment based on the acquired sensordetection information; and evaluate attribute information of a point atwhich the sensor detection information is acquired based on theplurality of indices which are derived.

(9) In the aspect of the above (1), the derivation unit derives aplurality of indices indicating an attribute of a surroundingenvironment on the vehicle by inputting the sensor detection informationacquired by the acquisition unit to a plurality of the evaluation unitsdefined beforehand in accordance with a purpose used in control, and theplurality of evaluation units include a first evaluation unit thatdetermines a control attribute reflected in control content of thevehicle using the sensor detection information and a second evaluationunit that determines an environmental attribute indicating an attributeof an environment for the point using the sensor detection information.

(10) In the aspect of the above (9), the first evaluation unitdetermines the control attribute more frequently than the secondevaluation unit.

(11) In the aspect of the above (9), an update unit that updates adefinition of the evaluation unit is further included, and the updateunit is able to add the attribute information which is output from theevaluation unit.

(12) In the aspect of the above (4), a data generation unit thatassociates the sensor detection information and the plurality of indiceswith the position information, and selectively generates transmissiondata which is transmitted to an outside in accordance with content ofthe plurality of indices is further included.

(13) In the aspect of the above (12), a communication unit that receivesa response signal sent back with respect to the transmission datatransmitted to the outside is further included, and theinformation-providing unit provides landscape information indicatinginformation relating to a landscape around the vehicle based on any ofthe attribute information determined by the vehicle and updatedattribute information included in the response signal.

(14) An information-processing device according to an aspect of thisinvention is an information-processing device including: an acquisitionunit that acquires sensor detection information indicating a detectionresult from a sensor mounted in a vehicle through communication; aderivation unit that derives a plurality of indices for a surroundingenvironment of the vehicle based on the sensor detection informationacquired by the acquisition unit; an evaluation unit that evaluatesattribute information of a point at which the sensor detectioninformation is acquired based on a plurality of indices derived by thederivation unit and attribute information acquired from the vehicle; andan information-providing unit that transmits a result of the evaluationevaluated by the evaluation unit to the vehicle.

Advantage of the Invention

According to (1), (7), (8), and (14), it is possible to evaluate theattribute information of the surrounding environment of the vehicle onthe basis of information of a landscape around the vehicle detected bythe vehicle.

According to (2), it is possible to evaluate a sensitivity index seenfrom the vehicle at a point where the vehicle is traveling in reality.

According to (3), it is possible to evaluate a sensitivity indexchanging at a time slot or in a season by evaluating the sensitivityindex from the vehicle at a time when the vehicle is traveling inreality.

According to (4), information of the evaluated sensitivity index isprovided to the vehicle, and thus an occupant can drive by selecting aroute according to the sensitivity index.

According to (5), an occupant can drive by selecting a category of alandscape with a good view and selecting a route with a good viewcorresponding to the category.

According to (6), an autonomously driven vehicle can travel with a goodview by changing a lane of a host vehicle or a positional relationshipwith another vehicle.

According to (9), the vehicle can use the derived plurality of indicesin traveling control of the vehicle or information provision of a route.

According to (10), the vehicle can control traveling assistance of thevehicle by determining an attribute relevant to traveling control at ahigh frequency.

According to (11), it is possible to obtain an evaluation result bysetting an attribute desired to be inspected by a user.

According to (12), the transmission data is not generated in a casewhere there is no calculated attribute value or a case where a conditionis not satisfied, and thus it is possible to improve communicationefficiency.

According to (13), it is possible to use attribute information updatedby an external server in addition to attribute information determined bythe vehicle, and to perform assistance of traveling control of thevehicle more reliably.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example of a configuration of aninformation-processing system 1 according to an embodiment.

FIG. 2 is a diagram illustrating an example of a configuration of anexternal sensing unit 110 according to the embodiment.

FIG. 3 is a diagram illustrating regions around a vehicle 100 which aredetected by each sensor according to the embodiment.

FIG. 4 is a diagram illustrating an example of content of detection data115 which is generated by an object recognition device 114 according tothe embodiment.

FIG. 5 is a diagram illustrating an example of a configuration of anavigation device 120 according to the embodiment.

FIG. 6 is a histogram illustrating a distribution of distances accordingto the embodiment.

FIG. 7 is a histogram illustrating a distribution of chroma according tothe embodiment.

FIG. 8 is a diagram illustrating a movement of a subject in continuouslycaptured images according to the embodiment.

FIG. 9 is a diagram illustrating an example of content of derived indexvalue data 222 according to the embodiment.

FIG. 10 is a diagram illustrating an example of content of POI data of aplace with a good view according to the embodiment.

FIG. 11 is a graph illustrating an example of a relationship betweentravel data of the vehicle 100 and a final evaluation value according tothe embodiment.

FIG. 12 is a flow chart illustrating an example of a flow of processeswhich are executed in the information-processing system 1 according tothe embodiment.

FIG. 13 is a configuration diagram in a case where an autonomouslydriven vehicle 300 is applied to an information-processing system 2according to the embodiment.

FIG. 14 is a diagram illustrating an example of a configuration of anautonomously driven vehicle 300 according to a modification example.

FIG. 15 is a diagram illustrating an example of content of data which isincluded in control attributes.

FIG. 16 is a diagram illustrating an example of content of data which isincluded in environmental attributes.

FIG. 17 is a diagram illustrating an example of a configuration of anexternal server 400.

DESCRIPTION OF THE EMBODIMENTS

Hereinafter, an embodiment of an information-processing device, aninformation-processing method, and a program of the present inventionwill be described with reference to the accompanying drawings.

FIG. 1 is a diagram illustrating an example of a configuration of aninformation-processing system 1 according to the embodiment. Theinformation-processing system 1 includes, for example, a vehicle 100 andan information-processing device 200. The vehicle 100 performs wirelesscommunication, and communicates with the information-processing device200 through a network NW. The information-processing device 200 acquiresa detection result based on a sensor mounted in the vehicle 100, andevaluates how good a view of a point where the vehicle 100 has traveledis.

[Vehicle]

The vehicle 100 includes, for example, an external sensing unit 110, anavigation device 120, a communication device 130, and a control unit140. The external sensing unit 110 acquires outside information using asensor, mounted in the vehicle 100, which senses the outside.

FIG. 2 is a diagram illustrating an example of a configuration of theexternal sensing unit 110. The external sensing unit 110 includes acamera 111, a radar device 112, a viewfinder 113, and an objectrecognition device 114 as sensors. These sensors are also used as, forexample, outside monitoring sensors for autonomous driving.

The camera 111 is a digital camera using a solid-state imaging elementsuch as, for example, a charge-coupled device (CCD) or a complementarymetal-oxide-semiconductor (CMOS). The camera 111 captures images of thevicinity of the vehicle 100. One or a plurality of cameras 111 areinstalled at any points on the vehicle 100, and capture images of thevicinity of the vehicle 100. In a case where a forward image iscaptured, the camera 111 is installed on the upper portion of the frontwindshield, the rear surface of the rear-view mirror, or the like.

In addition, in a case where a rearward image is captured, the camera111 is installed, for example, in the vicinity of the rear bumper. In acase where a crosswise image is captured, the camera 111 is installedat, for example, right and left side mirrors. The camera 111 may be, forexample, a stereo camera, installed at the roof of the vehicle 100,which captures an image of a landscape around 360°. The camera 111captures an image of the vicinity of the vehicle 100, for example,periodically repeatedly.

The radar device 112 radiates radio waves such as millimeter waves tothe vicinity of the vehicle 100, and detects radio waves (reflectedwaves) reflected from an object to detect at least the position(distance to and orientation of) of the object. One or a plurality ofradar devices 112 are installed at any points of the vehicle 100. Theradar device 112 may detect the position and speed of an object with afrequency-modulated continuous-wave (FMCW) system. In addition, adistance camera that measures a distance may be used in the measurementof a distance.

The viewfinder 113 is a light detection and ranging or laser imagingdetection and ranging (LIDAR) finder that measures scattered light withrespect to irradiation light and detects a distance to an object. One ora plurality of viewfinders 113 are installed at any points on thevehicle 100.

FIG. 3 is a diagram illustrating regions around the vehicle 100 whichare detected by each sensor. Here, (a) shows a detection region of asensor that detects the front, (b) shows a detection region of a sensorthat detects the right side, (c) shows a detection region of a sensorthat detects the rear, and (d) shows a detection region of a sensor thatdetects the left side. Forward, rearward, rightward, and leftwarddirections of the vehicle 100 can be sensed by each sensor of the camera111, the radar device 112, and the viewfinder 113 which are mounted inthe vehicle 100.

The object recognition device 114 recognizes the position, type, speed,or the like of an object outside of the vehicle 100 by performing asensor fusion process on detection results based on some or all of thecamera 111, the radar device 112, and the viewfinder 113. The objectrecognition device 114 recognizes states such as the position, speed,and acceleration of a nearby object or a structure, and recognizes anobject or the like around the vehicle 100 which is recognized. Theposition of a nearby object may be represented by a representative pointsuch as the centroid or a corner of the object, or may be represented bya region which is represented by the contour of the object. Examples ofobjects recognized by the object recognition device 114 include astructure, a building, trees, a guardrail, a telephone pole, a parkedvehicle, a pedestrian, other objects, and the like in addition to thenearby vehicle. Such a function is used when a nearby object of thevehicle 100 is recognized in autonomous driving.

The object recognition device 114 generates data detected by each sensoras detection data 115 together at a predetermined timing. The objectrecognition device 114 generates the detection data 115 sampled at apredetermined sampling interval. FIG. 4 is a diagram illustrating anexample of content of the detection data 115 which is generated by theobject recognition device 114. The detection data 115 includes, forexample, a vehicle position, a traveling direction, a detection resultof each sensor, a date and time, and the like. The detection data 115 isan example of sensor detection information indicating the detectionresult of the external sensing unit 110.

The vehicle position is data indicating a position where an image or thelike is acquired. The object recognition device 114 acquires positiondata for each sampling period from the navigation device 120, and setsthe acquired position data as a vehicle position. The travelingdirection data is data in which the traveling direction of the vehicle100 is recorded. The object recognition device 114 acquires thetraveling direction data from a change of the position data or the like.

A camera 1, a camera 2, . . . include image data captured in a pluralityof directions in the vicinity of the vehicle 100. A radar 1, . . .include data of results in which the radar device 112 has detected anobject in a plurality of directions in the vicinity of the vehicle 100.A viewfinder 1, . . . include data in which the viewfinder 113 hasdetected an object in a plurality of directions in the vicinity of thevehicle 100. The date and time data is information of a date and time atwhich an image, a detection result or the like is acquired.

FIG. 5 is a diagram illustrating an example of a configuration of thenavigation device 120. The navigation device 120 performs route guidancein accordance with a route along which the vehicle 100 travels to adestination. The navigation device 120 includes, for example, a globalnavigation satellite system (GNSS) receiver 121, a navigation HMI 122,and a route determination unit 123, and holds map information 126 in astorage unit 125 such as a hard disk drive (HDD) or a flash memory.

The GNSS receiver 121 specifies the position (latitude, longitude, oraltitude) of the vehicle 100 on the basis of a signal received from aGNSS satellite. The position of the vehicle 100 may be specified orcomplemented by an inertial navigation system (INS) in which an outputof a vehicle sensor 60 is used. The navigation device 120 generates theposition data or the traveling direction data of the vehicle 100 on thebasis of received data of the GNSS receiver 121.

The navigation HMI 122 includes a display device, a speaker, a touchpanel, a key, and the like. The navigation HMI 122 may be partly orwholly shared with the above-described HMI 110. The route determinationunit 123 refers to the map information 126 to determine a route to adestination (including, for example, information relating to a transitpoint during traveling to a destination) input by an occupant using thenavigation HMI 122, for example, from the position of the vehicle 100specified by the GNSS receiver 121 (or any input position).

The map information 126 is, for example, information in which a roadshape is represented by a link indicating a road and nodes connected bythe link. The map information 126 may include the curvature of a road,point of interest (POI) information, or the like. As described later,the POI includes information of a place with a good view which isacquired from the information-processing device 200.

The map information 126 may be updated at any time by accessing theinformation-processing device 200 through the communication device 130and the network NW. The map information 126 may have informationrelating to a net user's POI acquired through the network NW furtheradded thereto. The POI in a route during traveling may be displayed onthe navigation HMI 122.

The navigation device 120 performs route guidance using the navigationHMI 122 on the basis of a route determined by the route determinationunit 123. Meanwhile, the navigation device 120 may be realized by thefunction of a terminal device such as, for example, a smartphone or atablet terminal possessed by a user. In addition, the navigation device120 may transmit its current position and destination to theinformation-processing device 200 or other navigation servers (notshown) through the communication device 130, and acquire a route sentback from them.

The communication device 130 performs wireless communication, forexample, using a cellular network, a Wi-Fi network, Bluetooth(registered trademark), dedicated short-range communication (DSRC), orthe like, and communicates with the information-processing device 200through the network NW.

The control unit 140 transmits the detection data 115 indicating thedetection result detected by the external sensing unit 110 to theinformation-processing device 200 through the communication device 130and the network NW. In addition, the control unit 140 causes thenavigation HMI 122 to display information transmitted by theinformation-processing device 200 through the communication device 130.

The control unit 140 is realized by a processor such as a centralprocessing unit (CPU) executing a program (software). In addition, someor all of the control unit 140, the external sensing unit 110, and thenavigation device 120 may be realized by hardware such as a large-scaleintegration (LSI), and application-specific integrated circuit (ASIC),or field-programmable gate array (FPGA), or may be realized by softwareand hardware in cooperation.

[Information-Processing Device]

As shown in FIG. 1, the information-processing device 200 includes, forexample, an information acquisition unit 210, an index derivation unit220, a landscape evaluation unit 230, an information-providing unit 240,and a storage unit 260.

The index derivation unit 220, the landscape evaluation unit 230, andthe information-providing unit 240 are realized by a processor such as acentral processing unit (CPU) executing a program (software). Inaddition, some or all of these functional units may be realized byhardware such as a large-scale integration (LSI), anapplication-specific integrated circuit (ASIC), or a field-programmablegate array (FPGA), or may be realized by software and hardware incooperation. The program may be stored in a storage device such as ahard disk drive (HDD) or a flash memory in advance, may be stored in adetachable storage medium such as a DVD or a CD-ROM, or may be installedin the storage device by the storage medium being mounted in a drivedevice (not shown).

The information acquisition unit 210 includes, for example, a networkinterface card (NIC) for connection to the network NW. The informationacquisition unit 210 acquires the detection data 115 from the externalsensing unit 110 mounted in the vehicle through the network NW.

The index derivation unit 220 derives a plurality of indices indicatinghow good a view of a landscape around the vehicle 100 is on the basis ofthe detection data 115 acquired by the information acquisition unit 210.The index derivation unit 220 derives a plurality of indices byperforming an image analysis, an arithmetic operation or the like on thebasis of the detection data 115. A method of deriving a plurality ofindices will be described later in detail.

The landscape evaluation unit 230 evaluates how good a view of a pointat which the detection data 115 is acquired is on the basis of theplurality of indices derived by the index derivation unit 220. A methodof evaluating how good a view is will be described later in detail.

In addition, the information-providing unit 240 transmits information onhow good a view evaluated by the landscape evaluation unit 230 is to thevehicle 100 through the network NW. In a case where theinformation-processing device 200 is a navigation server, theinformation on how good a view evaluated by the landscape evaluationunit 230 is may be reflected in a route search result.

The storage unit 260 is realized by, for example, a random-access memory(RAM), a read-only memory (ROM), a hard disk drive (HDD), a flashmemory, a hybrid-type storage device in which a plurality of elementsamong them are combined, or the like. In addition, a portion or theentirety of the storage unit 260 may be an external device, such as anetwork-attached storage (NAS) or an external storage server, to whichthe information-processing device 200 is able to have access. Thestorage unit 260 has, for example, map information 261 and landscapeinformation 262 stored therein.

The map information 261 is, for example, information in which a roadshape is represented by a link indicating a road and nodes connected bythe link. The map information 261 includes the curvature of a road, POIinformation, or the like.

The landscape information 262 has information relating to how good aview evaluated by the landscape evaluation unit 230 is stored therein.The information relating to how good a view is stored in associationwith, for example, a plurality of coordinates (positions) where nodes orlinks stored in the map information 261 are present. The POI may beassociated with the coordinates.

[Information Generation Method]

Next, a method of generating information relating to how good a view isin the information-processing device 200 will be described. The indexderivation unit 220 refers to the detection data 115 acquired by theinformation acquisition unit 210, and derives multiple types of indexvalues X1 to Xn (n is any natural number) relating to how good a view isin association with positions.

The index derivation unit 220 derives, as an index value Xn, astatistical value such as an average of indices according to directionor a maximum value which is obtained in forward, rearward, rightward,and leftward directions in the vicinity of the vehicle 100 on the basisof data of each of a plurality of sensors. The index derivation unit 220may derive the index value Xn according to direction. The derivation ofthe index value Xn according to direction is, for example, thederivation of the index value Xn for each direction such as a forwarddirection from a driver's seat and a passenger seat, a direction outwardfrom the side window, or a rearward direction from the rear window.

The index derivation unit 220 derives an index value relating to, forexample, distance, transmittance, color, contrast, dynamics, altitude orthe like as an index indicating how good a view is.

Regarding the distance, the index derivation unit 220 derives an indexusing distance data of the radar device 112 recorded in the detectiondata 115. The index derivation unit 220 may use distance data obtainedby the external sensing unit 110 in the calculation of distance, orcalculate a distance corresponding to a pixel from a focal length or adifference in a change of image data based on a plurality of pieces ofimage data.

In a case where the index of distance is derived, the index derivationunit 220 calculates a distribution of distances detected in the forward,rearward, rightward, and leftward directions of the vehicle 100 on thebasis of the distance data. For example, when 360 degrees in thevicinity of a vehicle is surveyed, a distance is obtained for everyhorizontal angle, and a distribution of distances is calculated. Thisdistribution is represented in the form of a histogram. FIG. 6 is ahistogram illustrating a distribution of distances. The index derivationunit 220 derives an index value indicating the extent of a distributionof distances, for example, using a variance value of a distribution ofdistances as a parameter.

In a case where the index of transmittance is derived, the indexderivation unit 220 calculates a contrast index (a frequency component)of a pixel a predetermined distance or more away in an image. In thepixel a predetermined distance or more away, a portion obtained byimaging a predetermined distance or more among images (which may not bestereo images) using, for example, a stereo camera is extracted. Theindex derivation unit 220 performs a two-dimensional FFT processperpendicular to a horizontal direction with respect to an image of theextracted portion, and calculates a spatial frequency spectrum. Theindex derivation unit 220 uses the calculated spatial frequency spectrumas a contrast index of transmittance. A high-frequency component of alandscape portion of a predetermined range or more indicates a hightransmittance.

In a case where the contrast index is derived, the index derivation unit220 performs a two-dimensional FFT process similarly to the above withrespect to an image, calculates a spatial frequency spectrum, and usesthe spatial frequency spectrum as the contrast index of texture in theimage. In a case where the contrast index of texture in an image ishigh, a place where the image is captured is likely to be, for example,a landscape having a high proportion of texture of a forest, a bare rockor the like.

In a case where the color index of hue and chroma is derived, the indexderivation unit 220 calculates the hue and chroma of each pixel in animage. The index derivation unit 220 divides, for example, an image intoa plurality of regions, and calculates a distribution of hue and chromafor each region. This distribution is represented in the form of ahistogram. FIG. 7 is a histogram illustrating a distribution of chroma.The index derivation unit 220 uses the distribution of hue and chroma asa color index in an image.

The index derivation unit 220 compares the distributions of hue andchroma in the respective regions, and extracts a place where an image iscaptured as a spot characterized by hue in a case where a significantlydifferent characteristic distribution is obtained in surroundingregions. The height of an index value of color serves as an index inwhich a distribution of colors is characteristic with respect to thesurroundings. A region having a distribution characteristic with respectto surrounding regions in an image is likely to be, for example, alandscape characterized by the hue of a flower, the sea or the like.

In a case where an index relating to dynamics is derived, the indexderivation unit 220 derives an index using a plurality of pieces ofposition data and a plurality of pieces of image data. In a case where asubject is moving, an index relating to dynamics is derived on the basisof the amount of movement in an image of the subject.

The index derivation unit 220 calculates the movement (dynamics) of asubject from a difference in a change of image data on the basis of aplurality of images and movement data of the vehicle 100.

FIG. 8 is a diagram illustrating a movement of a subject in continuouslycaptured images. For example, in a case where an image is captured in(B) a predetermined time after an image captured in (A), a movementdistance L1 of a large subject P which is distant in (B) is smaller thana movement distance L2 of a subject Q located at a short distance. In acase where a region of the subject is large and a small movement isdetected with respect to a movement distance L of the vehicle 100, theindex derivation unit 220 may derive an index value using this movementas an index of dynamics.

The index derivation unit 220 extracts, for example, a subject having asize of a predetermined value or more on the basis of continuouslycaptured images. The index derivation unit 220 extracts, for example,the subject P on the basis of the image of (A) and the image of (B). Forexample, in a case where a ratio between an area Si in the image of thesubject P and an area S of the image (S1/S) exceeds a predeterminedthreshold, the index derivation unit 220 recognizes that the subject Pis a large subject.

The index derivation unit 220 calculates an index of movement of thesubject P on the basis of the image of (A) and the image of (B). Theindex of movement of the subject P is calculated on the basis of, forexample, a correlation between the movement distance L1 of the subject Pin the image of (A) and the image of (B) and the movement distance L ofthe vehicle while the image of (A) and the image of (B) are captured. Acase where a subject region is large in the index value of movement andthe movement distance L1 of the subject is smaller than the movementdistance L of the vehicle 100 serves as an index indicating that adistant landscape is imaged. The index value of dynamics serves as anindex of a scene in which a large subject such as a mountain, a tree, ora building comes into sight.

In a case where an index relating to altitude is derived, the indexderivation unit 220 uses three-dimensional position data of thenavigation device 120 which is recorded in the detection data 115. Theindex derivation unit 220 extracts a place having an altitude of apredetermined threshold or more. The index value of altitude serves asan index of a scene in which the field of vision capable of beingsurveyed extends.

FIG. 9 is a diagram illustrating an example of content of derived indexvalue data 222. The landscape evaluation unit 230 evaluates how good aview of a point at which the detection data 115 is acquired is on thebasis of a plurality of index values X1 to Xn derived by the indexderivation unit 220.

The landscape evaluation unit 230 extracts a distance of a predeterminedvalue or more on the basis of the index value of distance. A place ofwhich the index value of distance is a predetermined value or moreindicates that its space spreads, and a high relevance to a view isestimated. The landscape evaluation unit 230 groups a plurality of indexvalues X1 to Xn derived in the extracted place by combining the indexvalue Xn having relevance. A group is determined for each category of,for example, a landscape such as “mountain” or “seashore.”

The landscape evaluation unit 230 multiplies each grouped index value Xnby a weighting coefficient an. The landscape evaluation unit 230 addsthe index value Xn of a group by increasing a coefficient an of eachindex value Xn within a group in accordance with the synergistic effectof a group, and calculates a score (an evaluation value) for each groupas represented by Expression (1).

$\begin{matrix}{{{{Group}\mspace{14mu} {evaluation}\mspace{14mu} {{value}(1)}} = {{\left( {{\alpha \; {1 \cdot X}\; 1} + {\alpha \; {2 \cdot X}\; 2} + {\alpha \; {3 \cdot X}\; 3}} \right)\mspace{14mu} {group}\mspace{14mu} {evaluation}\mspace{14mu} {{value}(2)}} = \left( {{\alpha \; {4 \cdot X}\; 4} + {\alpha \; {5 \cdot X}\; 5}} \right)}},\ldots} & (1)\end{matrix}$

The synergistic effect of a group refers to the addition of a weight toa coefficient an of an index value Xn having relevance to a place havinga good view. For example, a weight is added to a coefficient an of anindex value such as, for example, color or transmittance in a case wherea landscape is “sea,” and a weight is added to a coefficient of an indexvalue such as, for example, distance, color, dynamics, or altitude in acase where a landscape is “mountain.” The landscape evaluation unit 230compares scores for each group, performs ranking, and selects a grouphaving a highest score as a final evaluation value on the basis ofExpression (2). The group having the highest score indicates arepresentative landscape of a point at which the detection data 115 isacquired.

Final evaluation value=MAX{(α1·X1+α2·X2+α3·X3),(α4·X4+α5·X5), . . . }  (2)

The landscape evaluation unit 230 adds a weight to a coefficient an inassociation with a season in which the detection data 115 is acquired onthe basis of the detection data 115 acquired during a predeterminedperiod, and thus a scene characteristic of a season may be selected. Forexample, the landscape evaluation unit 230 can increase weights ofcoefficients of distance, transmittance, color, and contrast in theseasons of spring and autumn, and select a place with a good view whichis characterized by the hue of a landscape of autumn leaves, cherryblossoms or the like.

The landscape evaluation unit 230 may evaluate how good a view is byadding a weight to a coefficient an in association with a position andtime at which the detection data 115 is acquired. For example, alandscape may change depending on a time slot such as sunrise or sunseteven in the same place. In a time slot such as sunrise or sunset, acharacteristic landscape having an increase in a tint of redchromaticity in an image appears. The landscape evaluation unit 230 mayadd, for example, a weight to a coefficient an of the index value Xn ofcolor in association with a time. Thereby, the landscape evaluation unit230 can select, for example, a place with a good view at a time at whichthe hue is characteristic, such as a time of sunset or sunrise on alakeside or seashore.

In a case where the index derivation unit 220 derives the index value Xnin each detection direction of each sensor, the landscape evaluationunit 230 may evaluate how good a view is by adding a weight to acoefficient an in association with a position at which the detectiondata 115 is acquired and the movement direction thereof. For example, ina case where the spread of a scene having a good view in a direction inwhich the vehicle 100 moves is desired to be evaluated, the landscapeevaluation unit 230 may add a larger weight to a coefficient an of theindex value Xn in a traveling direction than in other directions.

The landscape evaluation unit 230 may rank how good a view is on thebasis of the score of a group, and store information relating to howgood a view is for each category, as the landscape information 262, inthe storage unit 260. The categories are categories such as, forexample, “best views,” “colorful spots,” “landmark tour spots,”“mountains,” “seas,” or “night views.”

The landscape evaluation unit 230 may impart a spot name to theextracted place with a good view with reference to the map information261. The landscape evaluation unit 230 may impart a spot name to a placewith a good view on the basis of information retrieved through thenetwork NW. The landscape evaluation unit 230 generates informationrelating to how good a view is as POI data, and adds the generatedinformation to the map information 261. The network NW includes some orall of, for example, a wide area network (WAN), a local area network(LAN), the Internet, a dedicated channel, a wireless base station, aprovider, and the like.

FIG. 10 is a diagram illustrating an example of content of POI data 231of a place with a good view.

Further, the landscape evaluation unit 230 may generate data byassociating travel data of the vehicle 100 with a final evaluationvalue. FIG. 11 is a graph illustrating an example of a relationshipbetween travel data of the vehicle 100 and a final evaluation value. Thelandscape evaluation unit 230 associates the travel data with the finalevaluation value, and extracts a point at which the final evaluationvalue exceeds a predetermined threshold as a place with a good view. Thelandscape evaluation unit 230 may add data of a place with a good viewon an extracted travel route to the map information 261.

In the information-processing system 1, the evaluation of how good aview is may be performed on the basis of information sensed by aplurality of vehicles 100. In this case, the POIs of the landscapeinformation 262 and the map information 261 which are stored in thestorage unit 260 may be updated in real time by performing statisticalprocessing on the information sensed by the plurality of vehicles 100.

The information-providing unit 240 provides the vehicle 100 with the POIinformation on how good a view is which is stored in the map information261. For example, in a case where an occupant performs an operation ofsetting of a route to a destination in the navigation device 120 by theinformation-providing unit 240 providing information to the vehicle 100,information on a place with a good view is provided corresponding to theroute to a destination. Thereby, in the navigation HMI 122, a spothaving a good view is displayed on the route to a destination.

The information-providing unit 240 may select a place having a good viewin accordance with a category of good views which is selected by anoccupant of the vehicle 100 and provide the vehicle 100 with theselected place. In addition, the navigation device 120 may perform routesetting in accordance with the category of good views which is selectedby the occupant. When the occupant performs, for example, an operationon the navigation HMI 122, the occupant selects a category such as “bestviews,” “colorful spots,” or “landmark tour spots.” In a case where theoccupant selects, for example, a category of “best views,” thenavigation device 120 performs route setting in accordance with the“best views,” and thus the occupant can drive along a route with a goodview.

Next, processes which are executed in the information-processing system1 will be described. FIG. 12 is a flow chart illustrating an example ofa flow of processes which are executed in the information-processingsystem 1. First, the information acquisition unit 210 acquires thedetection data 115 from the vehicle 100 through the network NW (stepS100). The index derivation unit 220 derives a plurality of index valuesXn indicating how good a view is on the basis of the detection data 115(step S110).

The landscape evaluation unit 230 evaluates how good a view of a pointat which sensor detection information is acquired is on the basis of theplurality of index values Xn (step S120). The information-providing unitprovides the vehicle 100 with information relating to how good a view is(step S130).

As described above, according to the information-processing system 1, itis possible to automatically index how good a view is on the basis ofthe detection data 115 from the vicinity of the vehicle 100 which isdetected by the vehicle 100.

Thereby, according to the information-processing system 1, it ispossible to evaluate how good a view at a point at which the detectiondata 115 is acquired is in accordance with a position and time at whichthe detection data 115 is acquired.

In addition, according to the information-processing system 1, anoccupant can perform route setting in the navigation device 120 inaccordance with a category of good views, and the occupant can drivealong a route with a good view.

The above-described information-processing system 1 may be applied to anautonomously driven vehicle 300. FIG. 13 is a configuration diagram in acase where the autonomously driven vehicle 300 is applied to aninformation-processing system 2. A navigation device 320 outputs a routeto a destination to a recommended lane determination device 360. Therecommended lane determination device 360 refers to a map which is moredetailed than map data included in the navigation device 320, determinesa recommended lane in which a vehicle travels, and outputs therecommended lane to an autonomous driving control device 350.

The autonomous driving control device 350 controls some or all of adrive force output device 370 including an engine or a motor, a brakedevice 380, and a steering device 390 so as to travel along therecommended lane which is input from the recommended lane determinationdevice 360 on the basis of information which is input from an externalsensing unit 310.

Here, the autonomously driven vehicle 300 may change the recommendedlane on the basis of evaluation performed by the landscape evaluationunit 230. The autonomously driven vehicle 300 performs at least one of alane change or a change of a distance relationship with another vehicleso as to improve the evaluation performed by the landscape evaluationunit 230 of how good a view is. For example, the recommended lanedetermination device 360 may determine a recommended lane to which toperform a lane change or to return to an original lane after the lanechange so that the index from the landscape evaluation unit 230 of howgood a view is improves. In addition, in a case where a paralleltraveling vehicle or a nearby vehicle is present, the recommended lanedetermination device 360 may determine a recommended lane so as tochange a route, such as increasing an inter-vehicle distance, performinga lane change, or passing so that the index from the landscapeevaluation unit 230 of how good a view is improves.

In such an autonomously driven vehicle 300, it is assumed that there aremore opportunities for an occupant to view the landscape than in amanually driven vehicle. Therefore, by using the information-processingsystem 2 according to the embodiment, an occupant can select a route toa destination with a good view along a route in which autonomous drivingis being performed.

Modification Example

In the above embodiment, a case where the autonomously driven vehicle300 communicates with the information-processing device 200 and derivesan index value of how good a view is along a travel route of the vehiclehas been illustrated. An index value for the surrounding environment ofthe autonomously driven vehicle 300 may be derived on the autonomouslydriven vehicle 300 side rather than being derived in theinformation-processing device 200 provided outside of the autonomouslydriven vehicle 300. The index value for the surrounding environment ofthe autonomously driven vehicle 300 may be derived in accordance withthe position of the vehicle. In the following description, the samecomponents as those in the above embodiment (the number of devices andthe positions thereof may differ) are denoted by the same names andsigns, and the description thereof will not be given.

FIG. 14 is a diagram illustrating an example of a configuration of anautonomously driven vehicle 300 according to a modification example. Inthe modification example, the information-processing device 200 ismounted in the autonomously driven vehicle 300. Theinformation-processing device 200 is built into or connected outside of,for example, the navigation device 320. The outside connection refers tothe information-processing device 200 connected to the navigation device320 in a wired or wireless manner operating as a portion of the functionof the navigation device 320. Meanwhile, the autonomous driving controldevice 350 of the autonomously driven vehicle 300 (hereinafter alsoreferred to as a host vehicle) is an example of a first evaluation unit,and the information-processing device 200 is an example of a secondevaluation unit.

The autonomous driving control device 350 evaluates, for example,attributes of the environment around a vehicle on the basis of detectiondata 315 detected by the external sensing unit 310, and performstraveling assistance of avoidance control based on autonomous driving onthe basis of an evaluation result or traveling assistance of a vehiclefor avoiding a collision or an accident based on advanceddriver-assistance systems (ADAS) or the like. The detection data 315includes at least a portion of or the entirety of the content of thedetection data 115 (see FIG. 4).

The information-processing device 200 includes the informationacquisition unit 210, the landscape evaluation unit 230, theinformation-providing unit 240, and a communication unit 250. Theinformation-processing device 200 evaluates attributes of theenvironment around a vehicle on the basis of, for example, the detectiondata 315 detected by the external sensing unit 310 unlike the evaluationof attributes for traveling assistance of traveling of the vehicle, andassists the determination of a route of the vehicle which is performedby the navigation device 320 on the basis of an evaluation result.Meanwhile, in the modification example, the autonomous driving controldevice 350 and the information-processing device 200 are an example of aplurality of evaluation units. The autonomous driving control device 350is an example of the first evaluation unit, and theinformation-processing device 200 is an example of the second evaluationunit.

First, the evaluation of attributes which is performed by the autonomousdriving control device 350 will be described. The autonomous drivingcontrol device 350 acquires the detection data 315 (sensor detectioninformation) from the external sensing unit 310. The detection data 315includes, for example, position data acquired from a GNSS or dataacquired from another vehicle sensor in addition to data acquired fromthe external sensing unit 310.

The autonomous driving control device 350 stores data based on a sensorvalue such as a vehicle position, a movement speed, or a posture statequantity among the detection data 315 in a vehicle sensor data-holdingunit 351, and stores pixel information of captured image data calculatedon the basis of the detection data 315, difference information which isinformation of a difference in a plurality of pieces of detection data,and data such as a basic index value required for the derivation ofattribute information in a temporary memory 352. The basic index valueis a parameter which is mathematically specified in image processing.

The basic index value in this case may be a parameter which ismathematically specified in image processing without being limited to avalue such as “view” as in the above embodiment. The autonomous drivingcontrol device 350 determines a control attribute using the detectiondata 315. The control attribute is data including a combination of anattribute value indicating whether it corresponds to each environment ofa vehicle which is typified and an attribute index indicating its degreein the case of corresponding thereto.

FIG. 15 is a diagram illustrating an example of content of data includedin a control attribute 355. The control attribute 355 has an attributevalue or an attribute index associated with “attributes” obtained bytypifying the environment around a vehicle such as, for example,“collision, congestion, a curve, and an object.”

The attribute index is, for example, a set of parameters relevant to anattribute among a plurality of parameters obtained on the basis of thedetection data 315. For example, regarding the attribute of “curve,”parameters relevant to the curve such as “a lane, lane curvature, asteering angle, and acceleration” are extracted from data such as “aposition, a vehicle speed, acceleration, a yaw rate, a steering angle,another vehicle, a person, an object, a relative distance, a relativespeed, a relative angle, a lane, and a lane curvature” obtained by thedetection data 315, and are associated with the attribute index of thecurve.

A plurality of parameters are acquired on the basis of, for example, thedetection data 315. The detection data includes data recognized within apredetermined distance around a vehicle, data detected in the vehicle,and the like.

The attribute index indicates the degree of an attribute, and is, forexample, the magnitude of an attribute index value calculated on thebasis of the magnitude of a vector constituted by a plurality ofparameters extracted in association with an attribute.

The attribute value is, for example, an evaluation value for determiningwhether it corresponds to an attribute. The attribute value isrepresented by, for example, the binary value of 0 or 1 on the basis ofresults of comparison between an attribute index value and a threshold.For example, the attribute value is associated with 1 in the case ofcorresponding to an attribute, and is associated with 0 in the case ofnot corresponding to an attribute.

The autonomous driving control device 350 inputs, for example, datastored in the vehicle sensor data-holding unit 351 and data stored inthe temporary memory 352 to an evaluation function 354, and determineswhether it corresponds to an attribute on the basis of a calculationresult. The evaluation function 354 is constituted by, for example, amultilayer neural network having an intermediate layer.

The multilayer neural network is a multilayered neural network which isused in deep learning. The evaluation function 354 is set by, forexample, the execution of deep learning using learning data. Theevaluation function 354 is set by learning executed in advance.

The autonomous driving control device 350 inputs, for example, aplurality of images included in the data stored in the temporary memory352 to the evaluation function 354 in the derivation of the controlattribute around a vehicle, and arithmetically operates results ofcomparison with a feature amount when each pixel of an image is used asan input vector through the multilayer neural network.

In this case, the evaluation function 354 is configured to outputrelevance to each attribute to a new input vector by a weight of eachlayer being optimized and a feature amount corresponding to a correctanswer attribute being held as a result of learning a large number ofinput vectors and correct answer attributes at that time as teachingdata in advance. For example, the evaluation function 354 can beconfigured to include a boundary of a shape included in an image, anattribute of an object, and attribute information of the travelenvironment in attributes, and to include transition from the last timeor a state generated next when a plurality of time-series images areinput. The autonomous driving control device 350 continuously acquiresevaluation values of a vehicle, a road, a pedestrian, an object and ascene attribute for an input image using this evaluation function 354,and recognizes an outside state.

The autonomous driving control device 350 inputs data relating to avehicle stored in the vehicle sensor data-holding unit 351 to theevaluation function 354, calculates a plurality of parameters includinga recognized target, and extracts a plurality of parameters for eachattribute. The autonomous driving control device 350 calculates a score(an attribute index value) for each group using the evaluation function354.

The autonomous driving control device 350 compares the calculatedattribute index value with a criterion. The autonomous driving controldevice 350 determines the criterion adaptability of the attribute indexvalue using a comparison process in which the attribute index value isequal to or greater than a threshold, less than the threshold, within apredetermined range, or the like. In a case where the attribute indexvalue satisfies a criterion, the autonomous driving control device 350determines that it corresponds to an attribute, and allocates 1 to theattribute value. In a case where the attribute index value does notsatisfy the criterion, the autonomous driving control device 350determines that it does not correspond to an attribute, and allocates 0to the attribute value.

The autonomous driving control device 350 generates a control attributeon the basis of the content of determined attribute value (see FIG. 15).For example, in a case where the attribute value is 1 and corresponds toan attribute, the autonomous driving control device 350 calculatesadditional information with respect to this attribute. The additionalinformation will be described later.

The autonomous driving control device 350 generates a control attributevalue 356 by adding the additional information to the attribute indexvalue with respect to an attribute having an attribute value of 1. Thecontrol attribute value 356 is used in control of traveling assistanceof the host vehicle as described later.

Hereinafter, a specific method of calculating an attribute will bedescribed. For example, in a case where the attribute of “collision” isevaluated, the autonomous driving control device 350 calculatescollision probability as an attribute index, determines an attributevalue on the basis of the magnitude of the calculated collisionprobability, and determines whether it corresponds to the attribute ofcollision.

The autonomous driving control device 350 recognizes a moving objectaround the host vehicle by inputting a plurality of pieces of image datato the evaluation function 354 in, for example, the calculation of thecollision probability, and calculates the current positions of an objectand the host vehicle. In a case where a plurality of objects arerecognized, the autonomous driving control device 350 specifies the typeof each object, gives an ID to each object, and calculates theprobability of collision between each object and the host vehicle.

The autonomous driving control device 350 inputs data in which “avehicle speed, acceleration, a yaw rate, a position” and the like aredetected and recognized to the evaluation function 354, and performs thefuture estimation of a positional relationship between the moving objectand the host vehicle at a time after a predetermined time defined inadvance.

The autonomous driving control device 350 performs deep learning basedon the multilayer neural network using the evaluation function 354,executes the future estimation of a positional relationship between themoving object and the host vehicle in accordance with a set estimatedtime, and calculates the probability of collision between the hostvehicle and a nearby object on the basis of an overlapping ratio betweena position distribution which is an estimated region where the hostvehicle can be located and a position distribution which is a regionwhere the nearby object can be located.

At this time, the autonomous driving control device 350 calculates theprobability of collision between the host vehicle and the nearby objectby multiplying each binding of the multilayer neural network of theevaluation function 354 by a weight coefficient stored in a storage unitin advance.

In a case where the calculated collision probability and a threshold arecompared with each other, and the collision probability does not satisfya criterion, the autonomous driving control device 350 allocates 0 tothe attribute value, and determines that there is no collision. In acase where the collision probability satisfies the criterion, theautonomous driving control device 350 allocates 1 to the attributevalue, and determines that there is the possibility of collision.

For example, in a case where the possibility of collision is determined,the autonomous driving control device 350 generates the controlattribute value 356 including additional information such as “attribute:collision, collision possibility: present, target: another vehicle(ID=*), and position: (x1, y1).” The autonomous driving control device350 outputs the control attribute value 356 to the control unit such asthe drive force output device 370, the brake device 380, or the steeringdevice 390, and performs control of driving assistance such as avoidancefrom a target.

Similarly, the above-described autonomous driving control device canalso be configured to perform attribute determination and estimateadditional information with respect to a plurality of attributes of“preceding vehicle course change,” “pedestrian crossing,” and “signalstate change.” For example, the above-described autonomous drivingcontrol device 350 determines a control attribute more frequently thanthe information-processing device 200 to be described later. This isbecause it is necessary to frequently sense the surrounding environmentof the autonomously driven vehicle 300 for assistance of autonomousdriving control of the autonomous driving control device 350.

Next, the evaluation of an attribute which is performed by theinformation-processing device 200 will be described. Here, the processof the index derivation unit 220 according to the above embodiment isassumed to be performed by the landscape evaluation unit 230 instead.The information-processing device 200 evaluates, for example, theattribute of the environment around the host vehicle while the hostvehicle is traveling. In the information-processing device 200, theinformation acquisition unit 210 further includes an informationselection unit 211.

The information selection unit 211 includes, for example, twoinformation routes of a high-speed unit 212 which is a bus on ahigh-speed side for inputting and outputting data at high speed and alow-speed unit 213 which is a bus on a low-speed side to be used inoccasional reading.

The high-speed unit 212 is used in, for example, communication having alarge data capacity between the autonomous driving control device 350and the landscape evaluation unit 230. The high-speed unit 212 performs,for example, communication of data acquired by the external sensing unit310, the GNSS, the vehicle sensor 60, or the like of the autonomouslydriven vehicle 300.

For example, in a case where sixty images are captured by the externalsensing unit 310 during a predetermined time unit (AT), data reduced toabout ten images is transmitted to the landscape evaluation unit 230through the high-speed unit 212. The predetermined time unit is apredetermined time length which is set when a data set is generated asdescribed later.

The data set is not generated in a predetermined time unit, and may begenerated by delimiting data of an image by the partition of apredetermined image. The data delimited by the partition of an image maybe used in, for example, the calculation of a convolutional neuralnetwork.

The low-speed unit 213 is, for example, an information route when thelandscape evaluation unit 230 reads out an attribute value generated bythe autonomous driving control device 350 at any time. The amount ofinformation which is transmitted to and received from the low-speed unit213 is smaller than the amount of information which is transmitted toand received from the high-speed unit 212.

The low-speed unit 213 is also used in communication between theautonomously driven vehicle 300 and another device provided outside. Theautonomous driving control device 350 outputs, for example, the controlattribute value 356 generated at a predetermined timing, as transmissiondata, to a communication data generation and storage unit 233 (a datageneration unit) through the low-speed unit 213. The communication datageneration and storage unit 233 transmits, for example, the generatedtransmission data to an external server 400 through the communicationunit 250 connected to the low-speed unit 213.

The landscape evaluation unit 230 includes, for example, a definitionupdate unit 232 and the communication data generation and storage unit233. The landscape evaluation unit 230 determines an environmentalattribute indicating the attribute of the environment for a point thatis a target for evaluation using the detection data 315. Theenvironmental attribute is data including an attribute corresponding toeach typified environment around a vehicle and an attribute indexindicating the degree of the attribute.

FIG. 16 is a diagram illustrating an example of content of data includedin an environmental attribute 255. Here, the environmental attribute 255is generated mainly as a sensitivity index relevant to an occupant'ssensitivity. The sensitivity index is a mathematically specifiedparameter indicating an occupant's sensitivity for the environment of apoint that is a target for evaluation such as “view.” The sensitivityindex is calculated with respect to the attribute of a view, a ridecomfort, a congestion status, or the like.

The landscape evaluation unit 230 acquires, for example, informationstored in the temporary memory 352 and information stored in the vehiclesensor data-holding unit 351 through the high-speed unit 212 of theinformation acquisition unit 210, inputs the acquired information to anevaluation function 234, and determines a plurality of environmentalattributes for the surrounding environment at a position where theautonomously driven vehicle 300 is traveling while the vehicle istraveling. The evaluation function 234 is constituted by, for example, amultilayer neural network having an intermediate layer. The landscapeevaluation unit 230 calculates an attribute index for an attribute usingthe evaluation function 234. The landscape evaluation unit 230determines an attribute value on the basis of the calculated attributeindex. The landscape evaluation unit 230 evaluates the landscape of thesurrounding environment on the basis of the determined attribute valueand the magnitude of the attribute index.

In a case where the landscape evaluation unit 230 evaluates, forexample, “comfort of a road,”, data of a plurality of parametersrelevant to the comfort of a road such as acceleration, vibration,sound, congestion, or stop frequency in the detection data 315 is inputto the evaluation function 234. The landscape evaluation unit 230calculates, for example, the attribute index of comfort on the basis ofa change pattern of a characteristic parameter of the “comfort of aroad” which is learned in the evaluation function 234. The landscapeevaluation unit 230 determines whether a point that is a target forevaluation corresponds to the attribute of the “comfort of a road” onthe basis of the calculation result of the attribute index.

The landscape evaluation unit 230 determines whether an attribute indexvalue which is calculated on the basis of the magnitude of the attributeindex satisfies a criterion, and determines the attribute value of the“comfort of a road.” The landscape evaluation unit 230 determines thecriterion adaptability of the attribute index value using a comparisonprocess in which the attribute index value is equal to or greater than athreshold, less than the threshold, within a predetermined range, or thelike.

Specifically, the landscape evaluation unit 230 compares the magnitudesof vectors of the derived plurality of attribute index values withthresholds applied to respective attributes, calculates attributevalues, and calculates a degree at which a point that is a target forevaluation corresponds to a predetermined attribute. The attribute valuemay be represented by the binary value of 0 or 1 indicating whether itcorresponds to an attribute, or may be represented by a gradualnumerical value of a binary value or more.

The landscape evaluation unit 230 generates the environmental attribute255 of a point that is a target for evaluation in accordance with thedegree of the attribute value (see FIG. 16). For example, in a casewhere the attribute value satisfies the criterion and corresponds to anattribute, the landscape evaluation unit 230 generates an environmentalattribute value 236 by adding additional information to the attributeindex value with respect to this attribute. The landscape evaluationunit 230 generates the environmental attribute value 236 includingadditional information such as “attribute: comfort of a road, comfort:slight comfort, and position: (x1, y1).” The landscape evaluation unit230 derives the evaluation value of the sensitivity index at a pointthat is a target for evaluation in association with a position at whichthe detection data 315 is acquired on the basis of vehicle informationincluding a plurality of indices and position information. The landscapeevaluation unit 230 outputs the generated environmental attribute value236 to the communication data generation and storage unit 233.

The definition update unit 232 appropriately adds or changes attributeinformation in which an attribute is defined. The definition update unit232 adds or changes the attribute information on the basis of, forexample, input information accepted by a user's input operation. Theuser inputs, for example, a category of any attribute desired to beinspected by the user with respect to the environment around a vehicle.The definition update unit 232 updates the definition of an attributewhich is used in the evaluation function 234.

The landscape evaluation unit 230 may set an attribute desired to beinspected by the user on the basis of, for example, information which isinput to the definition update unit 232 by the user, and derive anevaluation result.

The communication data generation and storage unit 233 selectivelygenerates transmission data transmitted to the outside. The transmissiondata is transmitted to, for example, the external server 400 to bedescribed later. The communication data generation and storage unit 233generates one communication data set (transmission data) in apredetermined time unit (AT).

The communication data generation and storage unit 233 associates, forexample, the environmental attribute value 236 in a predetermined timeunit, position information, time information, a representative value ofthe detection data 315, and a time stamp including representativeinformation of a time with each other, generates the associated resultas a data set of transmission data, and stores the generated result in astorage unit. In this case, the position information may be informationof the section of a road specified by nodes and links in mapinformation.

The communication data generation and storage unit 233 transmits thetransmission data in a data set unit to the autonomous driving controldevice 350, the external server 400 to be described later which isprovided outside, or the like. However, for example, in a case where theattribute value is not present or the attribute value is less than athreshold, the communication data generation and storage unit 233 doesnot generate the transmission data. The communication data generationand storage unit 233 selectively generates the transmission datatransmitted to the outside on the basis of the content of the calculatedattribute value.

The communication data generation and storage unit 233 transmits, forexample, data relating to the attribute of the environment around theautonomously driven vehicle 300 sequentially for each predetermined timeunit to the autonomous driving control device 350 side through thelow-speed unit 213. According to the above-described configuration, theautonomously driven vehicle 300 can evaluate a plurality of indices forthe surrounding environment at the coordinates (position) of the vehiclewhile moving.

The external server 400 having the function of theinformation-processing device 200 may be further connected to theautonomously driven vehicle 300 in which the above-describedinformation-processing device 200 is mounted through the network NW. Thecommunication data generation and storage unit 233 transmits thetransmission data to the external server 400 through the communicationunit 250 capable of communication with the outside which is connected tothe low-speed unit 213. Using such a process, the autonomously drivenvehicle 300 can evaluate the environmental attribute even in an area inwhich communication conditions are not good.

FIG. 17 is a diagram illustrating an example of a configuration of theexternal server 400. The external server 400 acquires informationrelating to the environmental attribute value from the autonomouslydriven vehicle 300, re-evaluates the environmental attribute by addingfluctuating information, and provides an evaluation result to theautonomously driven vehicle 300.

For example, since the environmental attribute value of “view” or thelike fluctuates depending on the construction of a building, theweather, the air temperature, the shape of a vehicle, or the like, theexternal server 400 acquires information relating to the environmentfrom a vehicle which is traveling in the spot in reality, re-evaluatesthe environmental attribute by adding information such as the weather,the air temperature or the like, and also provides information to theautonomously driven vehicle 300 or another vehicle.

The external server 400 includes an information acquisition unit 410, anenvironment evaluation unit 420, and an information-providing unit 430.The environment evaluation unit 420 includes an evaluation function 422,an environmental information-holding unit 423, and an update unit 424that adds derived attribute information.

The environmental information-holding unit 423 stores vehicle geometryinformation including three-dimensional terrain information of a regionwhich is a target for evaluation, weather and air temperatureinformation, or information such as the dimensions of a plurality ofvehicles. The above information is temporal information that can bechanged over time, and the environment evaluation unit 420 acquires thetemporal information through the network NW at a predetermined timing,and updates the temporal information in a case where there is a change.

The environment evaluation unit 420 acquires the transmission datatransmitted by the communication data generation and storage unit 233 asreceived data 421 through the information acquisition unit 410. Theenvironment evaluation unit 420 inputs the temporal information storedin the environmental information-holding unit 423 to the evaluationfunction 422 in addition to the received data 421, and derives theenvironmental attribute value of the environment around the vehicle.

For example, a multilayer neural network is used in the evaluationfunction 422. The environment evaluation unit 420 calculates, forexample, the environmental attribute in accordance with a difference ina terrain, weather and air temperature, the dimensions of the vehicle,or the like.

For example, in the evaluation of the attribute of “view,” in a casewhere the weather of a point with a good view is rainy or cloudy, or theview becomes poor depending on a seating position in a vehicle, wherebythe environmental attribute value acquired from the autonomously drivenvehicle 300 indicates that “a view is poor,” the environment evaluationunit 420 changes the environmental attribute value of the “view” of acurrent point that is a target for evaluation using the evaluationfunction 422. The evaluation function 422 increases a weight coefficientfor a parameter in an arithmetic operation of a multilayer neuralnetwork in accordance with an environment change of an environment, aviewpoint position or the like such as a change in the weather or achange of a seating position of a vehicle.

In a case where the derived environmental attribute value of a pointthat is a target for evaluation becomes greatly different from theenvironmental attribute value of the received data, the update unit 424updates the definition of the evaluation function 422, and adds thederived environmental attribute value as a new environmental attributevalue. The update unit 424 generates update information 405 includingthe updated environmental attribute value. The update unit 424 transmitsthe update information 405 to the autonomously driven vehicle 300.

The update unit 424 provides information to another autonomously drivenvehicle. The update unit 424 transmits a response signal 406 through theinformation-providing unit 430. The response signal 406 is, for example,information including the update information 405 derived in the externalserver 400 on the basis of the transmission data.

In this manner, the autonomously driven vehicle 300 transmits thetransmission data through the communication unit 250, and thus canreceive the response signal 406, sent back with respect to thetransmission data from the external server 400, through the informationacquisition unit 210.

In the autonomously driven vehicle 300, in a case where the responsesignal 406 is received, the information-providing unit 240 provides theautonomously driven vehicle 300 with the landscape informationindicating information relating to the landscape around the autonomouslydriven vehicle 300 on the basis of any of attribute informationdetermined on the autonomously driven vehicle 300 side and addedattribute information included in the response signal 406 acquired froman external server.

Thereby, the autonomously driven vehicle 300 can also use the attributeinformation updated in the external server 400 in addition to theattribute information determined in the autonomously driven vehicle 300.The external server 400 can provide the shape of a vehicle orenvironmental attributes according to a change in the environment overtime such as a future change in the weather. According to theinformation-processing device 200, it is possible to perform assistanceof traveling control of autonomous driving of the autonomously drivenvehicle 300 more reliably.

While preferred embodiments of the invention have been described andillustrated above, it should be understood that these are exemplary ofthe invention and are not to be considered as limiting. Additions,omissions, substitutions, and other modifications can be made withoutdeparting from the scope of the present invention. Accordingly, theinvention is not to be considered as being limited by the foregoingdescription, and is only limited by the scope of the appended claims.For example, the index derivation unit 220 and the landscape evaluationunit 230 of the information-processing device 200 may be provided on thevehicle 100 side.

What is claim is:
 1. An information-processing device, comprising: anacquisition unit that acquires sensor detection information indicating adetection result from a sensor mounted in a vehicle; a derivation unitthat derives a plurality of indices for a surrounding environment basedon the sensor detection information acquired by the acquisition unit;and an evaluation unit that evaluates attribute information of a pointat which the sensor detection information is acquired based on theplurality of indices derived by the derivation unit.
 2. Theinformation-processing device according to claim 1, wherein theevaluation unit derives an evaluation value of a sensitivity index atthe point in association with a position at which the sensor detectioninformation is acquired based on the plurality of indices and vehicleinformation including position information.
 3. Theinformation-processing device according to claim 2, wherein theevaluation unit derives the evaluation value of the sensitivity index inassociation with a time included in a unit of a predetermined timelength.
 4. The information-processing device according to claim 2,further comprising an information-providing unit that provides thevehicle with information relating to a landscape based on the evaluationvalue.
 5. The information-processing device according to claim 4,wherein the information-providing unit provides the vehicle with theinformation relating to the landscape in accordance with relevancebetween a category of good views selected by an occupant of the vehicleand the attribute information.
 6. The information-processing deviceaccording to claim 1, wherein the vehicle is an autonomously drivenvehicle, and the vehicle performs at least one of a lane change or achange of a distance relationship with another vehicle so as to improveevaluation performed by the evaluation unit of how good a view is.
 7. Aninformation-processing method causing a computer to: acquire sensordetection information indicating a detection result from a sensormounted in a vehicle; derive a plurality of indices for a surroundingenvironment based on the acquired sensor detection information; andevaluate attribute information of a point at which the sensor detectioninformation is acquired based on the plurality of indices which arederived.
 8. A program causing a computer to: acquire sensor detectioninformation indicating a detection result from a sensor mounted in avehicle; derive a plurality of indices for a surrounding environmentbased on the acquired sensor detection information; and evaluateattribute information of a point at which the sensor detectioninformation is acquired based on the plurality of indices which arederived.
 9. The information-processing device according to claim 1,wherein the derivation unit derives a plurality of indices indicating anattribute of a surrounding environment on the vehicle by inputting thesensor detection information acquired by the acquisition unit to aplurality of the evaluation units defined beforehand in accordance witha purpose used in control, and the plurality of evaluation units includea first evaluation unit that determines a control attribute reflected incontrol content of the vehicle using the sensor detection informationand a second evaluation unit that determines an environmental attributeindicating an attribute of an environment for the point using the sensordetection information.
 10. The information-processing device accordingto claim 9, wherein the first evaluation unit determines the controlattribute more frequently than the second evaluation unit.
 11. Theinformation-processing device according to claim 9, further comprisingan update unit that updates a definition of the evaluation unit, theupdate unit being able to add the attribute information which is outputfrom the evaluation unit.
 12. The information-processing deviceaccording to claim 4, further comprising a data generation unit thatassociates the sensor detection information and the plurality of indiceswith the position information, and selectively generates transmissiondata which is transmitted to an outside in accordance with content ofthe plurality of indices.
 13. The information-processing deviceaccording to claim 12, further comprising a communication unit thatreceives a response signal sent back with respect to the transmissiondata transmitted to the outside, wherein the information-providing unitprovides landscape information indicating information relating to alandscape around the vehicle based on any of the attribute informationdetermined by the vehicle and updated attribute information included inthe response signal.
 14. An information-processing device, comprising:an acquisition unit that acquires sensor detection informationindicating a detection result from a sensor mounted in a vehicle throughcommunication; a derivation unit that derives a plurality of indices fora surrounding environment of the vehicle based on the sensor detectioninformation acquired by the acquisition unit; an evaluation unit thatevaluates attribute information of a point at which the sensor detectioninformation is acquired based on a plurality of indices derived by thederivation unit and attribute information acquired from the vehicle; andan information-providing unit that transmits a result of the evaluationevaluated by the evaluation unit to the vehicle.